Instructions to use wangzhen-w/PanoVLN_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wangzhen-w/PanoVLN_base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="wangzhen-w/PanoVLN_base")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, Qwen3_5ForConditionalGenerationForPanoVLN

processor = AutoProcessor.from_pretrained("wangzhen-w/PanoVLN_base")
model = Qwen3_5ForConditionalGenerationForPanoVLN.from_pretrained("wangzhen-w/PanoVLN_base", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wangzhen-w/PanoVLN_base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wangzhen-w/PanoVLN_base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wangzhen-w/PanoVLN_base",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/wangzhen-w/PanoVLN_base

SGLang

How to use wangzhen-w/PanoVLN_base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "wangzhen-w/PanoVLN_base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wangzhen-w/PanoVLN_base",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "wangzhen-w/PanoVLN_base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wangzhen-w/PanoVLN_base",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use wangzhen-w/PanoVLN_base with Docker Model Runner:
```
docker model run hf.co/wangzhen-w/PanoVLN_base
```

wangzhen-w

nielsr HF Staff commited on Oct 1

Commit

bb6f312

1 Parent(s): 3b7c995

Add pipeline tag, library name, and links to paper and code (#1)

Browse files

- Add pipeline tag, library name, and links to paper and code (726b5b34896bd463da7c8b98f1954499f9f392a0)

Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +13 -2

README.md CHANGED Viewed

@@ -1,6 +1,17 @@
 ---
 license: other
 license_name: matterport-academic-use
-license_link: >-
-  https://matterport.com/legal/matterport-end-user-license-agreement-academic-use-model-data
 ---

 ---
 license: other
 license_name: matterport-academic-use
+license_link: https://matterport.com/legal/matterport-end-user-license-agreement-academic-use-model-data
+library_name: transformers
+pipeline_tag: image-text-to-text
 ---
+# PanoVLN
+**PanoVLN** is a vision-and-language navigation policy that follows instructions from 360° RGB observations. It combines a Qwen3.5-4B backbone with PanoVGGT geometry features and predicts 18-action sequences. Confidence-guided execution selects how far to move before observing and planning again.
+- Paper: [PanoVLN: Towards Effective Panoramic Vision-and-Language Navigation](https://huggingface.co/papers/2609.34759)
+- Project page: [https://wangzhen-w.github.io/PanoVLN/](https://wangzhen-w.github.io/PanoVLN/)
+- GitHub: [https://github.com/wangzhen-w/PanoVLN](https://github.com/wangzhen-w/PanoVLN)
+This checkpoint is intended for simulation and real-world navigation experiments. For more details on training, evaluation, and deployment, please refer to the [repository documentation](https://github.com/wangzhen-w/PanoVLN).